Summary

This PhD dissertation explores the role of emerging fields, such as Artificial Intelligence (AI), for two key strategic decisions—entrepreneurial resource acquisition and alliance formation—and examines how these fields can be identified using computational methods. Chapter 2 examines the effects of being associated with an emerging field on initial venture financing from both scientists’ and investors’ perspectives. We demonstrate that increasing scientific involvement in a field positively influences capital raised, while increased investor involvement has a negative effect. This contrast likely arises because scientific interest signals innovation potential, whereas greater investor presence may indicate that the most lucrative opportunities have already been exploited. Chapter 3 investigates how the novelty of these fields—both to the firm making a decision and to its competitors—affects alliance formation. We further explore how the scientific background of alliance decision-makers shapes these strategic choices. Our findings show that only fields familiar to competitors of the focal firm positively impact alliance formation, while decision-makers with a scientific background tend to favor more novel solutions. Chapter 4 assesses the effectiveness of various computational methods in identifying fields, introducing a novel quantitative measure of overlap to compare their results. Our findings reveal that each method yields different insights, highlighting the need for careful interpretation as certain outputs may lack substantive meaning, necessitating human judgment. Chapter 5 extends traditional metrics, commonly applied to track topics over time within a single method, by using them to bridge topics, including fields, across different methods. We also assess the performance of traditional versus neural topic modeling in detecting emergence. Our results show that content-based metrics are particularly effective for cross-method matching, with traditional topic modeling demonstrating greater accuracy than neural topic modeling in identifying emerging fields.